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Related Experiment Video

Updated: May 3, 2026

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A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.

Sandeep Saharan1, Niyaz Ahmad Wani2, Shreeya Chatterji2

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India. sandeepsaharan@outlook.com.

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Summary
This summary is machine-generated.

The DXAIB system uses Artificial Intelligence (AI) to accurately detect breast cancer by combining Convolutional Neural Networks (CNNs) and Random Forest (RF) models. It enhances trust through explainable AI (XAI) methods like SHAP, providing clear diagnostic reasoning.

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Machine Learning for Disease Detection

Background:

  • Artificial Intelligence (AI) offers transformative potential in healthcare, but its "black box" nature hinders trust and adoption.
  • Deep learning models show high performance but often lack transparency in their decision-making processes.
  • Skepticism regarding AI interpretability limits its practical application in clinical settings.

Purpose of the Study:

  • To introduce DXAIB, a novel hybrid AI scheme for accurate breast cancer detection.
  • To address the critical challenge of AI interpretability in medical diagnostics.
  • To enhance transparency and build confidence in AI-driven medical decision-making.

Main Methods:

  • A hybrid methodology integrating Convolutional Neural Networks (CNNs) for feature learning and Random Forest (RF) for classification.
  • Implementation of the DXAIB scheme featuring convolutional layers for automated feature extraction.
  • Utilization of SHapley Additive exPlanations (SHAP) for local and global interpretability of AI predictions.

Main Results:

  • The DXAIB scheme achieved superior prediction outcomes compared to existing state-of-the-art methods.
  • Demonstrated effective breast cancer detection through the hybrid CNN-RF approach.
  • Provided comprehensive, level-specific explanations for AI-driven diagnostic predictions using SHAP.

Conclusions:

  • DXAIB offers a promising solution for accurate and interpretable breast cancer detection.
  • The integration of SHAP significantly enhances the transparency and trustworthiness of AI in medical diagnostics.
  • DXAIB represents a significant advancement in explainable AI (XAI) for healthcare applications.